[WIP] New Ray Serve tutorial #3709
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3709
Note: Links to docs will display an error until the docs builds have been completed. This comment was automatically generated by Dr. CI and updates every 15 minutes. |
crypdick
commented
Dec 18, 2025
| *.egg-info/ | ||
| .installed.cfg | ||
| *.egg | ||
| .git |
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Needed due to a bug in Ray; working on a PR
marwan116
reviewed
Jan 5, 2026
| "downscale_delay_s": 30, # Wait 30s before scaling down. | ||
| }, | ||
| # Max concurrent requests per replica before queueing. | ||
| # If the queue fills the shared cluster memory, future requests are backpressured until memory is freed. |
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This statement is confusing to me about Ray Serve's backpressure mechanism. Ray Serve relies on max_queued_requests to backpressure requests and not the cluster memory. See the docs for more detail
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Adds a new tutorial serving Pytorch models on Ray Serve.
This should be merged after #3694 since otherwise the new Ray version will break the existing Tune tutorial.
I will remove the [WIP] label after the Ray Serve team finishes their review.